In the field of radar signal processing, a technique known as “pulse compression” has long been used to improve the range resolution of radars. In general, pulse compression involves modulating a transmitted radar pulse (i.e., with a “code” or “waveform”) and then correlating the received signal with an appropriate “filter” function, based on the known modulation.
One reason for implementing a pulse compression system is the desire to obtain the high range resolution of a short pulse, while realizing the higher signal-to-noise ratio (SNR) of a longer uncoded pulse. This is accomplished by increasing the bandwidth of the longer pulse by introducing the signal modulation. This technique can extend the maximum detectable range, improve the probability of detection (PD), and affect a lower probability of intercept (LPI), by lowering the peak power requirements for the same SNR.
A major principle in radar is the coherent combination of signals in order to affect what signals are seen and what signals are suppressed in order to separate targets from clutter. One manifestation of this coherent combination is in spatial domain processing, which takes the form of azimuth and elevation transmit/receive antenna beams. Another manifestation is in the time domain, in which targets in a range cell of limited size are enhanced while targets outside this range cell are suppressed. In this second form there is a correspondence between the range cell size (resolution) and the signal bandwidth in the frequency domain. In all three domains (i.e. spatial, temporal and frequency) there will be unwanted “sidelobes” that can be sources of interference and false-targets. The present disclosure relates to pulse compression aspects, which include both time and frequency components.
The set of performance measures that determine the design of the code and the associated filters used in pulse compression include SNR loss, code amplitude, peak response broadening, and sidelobe behavior. The design of such codes and filters constitutes a tradeoff among the various performance measures. Optimized pulse compression search techniques have been developed that can compute many codes and filter combinations in response to each set of performance requirements. The corresponding filters can be “matched” to the codes in length/time and in amplitude and phase, so as to improve SNR gain and resolution, or “mismatched” in length/time, amplitude and phase, so as to reduce the correlation of sidelobes. One prior method of minimizing sidelobes by optimizing matched filter codes is described in “Multi-parameter Local Optimization for the Design of Superior Matched Filter Polyphase Pulse Compression Codes,” by Nunn and Welch. One benefit of using a matched filter is that it maximizes the gain in the SNR (i.e., the processing gain). Hence, the matched filter has no SNR loss because its filter characteristics are precisely matched to the received waveform. Conversely, when implementing a mismatched filter to reduce correlation sidelobes, some of this SNR processing gain and/or target resolution may be lost.
Synthetic aperture radar (SAR) is a particular type of radar that uses a plurality of small, low-directivity, stationary antennas scattered near or around the target area, or an antenna moving over stationary targets. Echo waveforms received by the moving or plurality of antennas can be processed to resolve the target. In some cases, SAR radar may be improved by combining many radar pulses to form a synthetic aperture, using additional antennas or significant additional processing. SAR operation typically involves transmitting signals that cover a broad spectrum, or frequency bandwidth, to obtain desirable resolution (e.g., 200 MHz to 2 GHz). For example, in SAR applications, the bandwidth occupied by the radar is so large that it overlaps with large swaths of heavily utilized and important spectral regions. These applications tend to cause heavy in-band, and sometimes out-of-band, spectral interference.
The demands and prevalence of modern electronic communications, navigation, and other systems make it difficult to obtain large swaths of contiguous bandwidth. In the real world, spectrum is a precious commodity that is carefully managed. For example, it may be necessary to satisfy spectrum managers that restrict transmission frequency bandwidth. Although radar designers have developed various methods to reduce time sidelobes, they have been less successful at minimizing spectral sidelobes and in band spectral properties. As a result, many of these pulse compression codes have not been heavily utilized in real world radar systems because of their spectral shortcomings. For general pulse compression applications, these out-of-band spectral emissions can interfere with other communications or radar devices at nearby frequencies. In broadband applications, the problem is even more serious. These spectral interference problems are both time and location dependant.
Thus, for many applications, most notably SAR applications, it is important to have available methods to rapidly and simultaneously control both the time sidelobes and spectral characteristics of these transmit waveforms. Nunn has developed methods to achieve fine control over time sidelobe code characteristics, such as peak sidelobe levels (PSLs) or integrated sidelobe levels (ISLs) of discrete, constant amplitude pulse compression codes using constrained optimization techniques. Nunn has also used the same methodology to create mismatched filters with excellent ISL, PSL and loss characteristics. Previous to the current effort, these methods have not been used to address the spectral issues.
Accordingly, there is a need for improved techniques for suppressing radar sidelobes by using time and spectral control. The systems and methods of the present disclosure solve one or more of the problems set forth above and/or other problems in the art.